In this decade, with the rise of data science accompanying the growth of e-commerce, many technologies have been developed. An example of these technologies is Blockchain, which has appeared to overcome security problems potentially. This research assesses Blockchain's implementation in supply chains through a methodology that uses deep learning and agent-based simulation. A case study was utilized to observe and validate research developments. The unique method predicts intrusions by using deep learning, and agent-based modeling reproduces artificial but convincing agents (e.g., customers, companies, hackers, and cyber pirates) in a computer-generated market. Trust and other relationships are systematically captured to represent Blockchain additions. Once again, the agent-based simulation model's environment permits hypothetical interactions and emergent features by coordinating supply and demand for business-to-consumer e-commerce events. The case study based on a real environment shows that the proposed method can determine the feasibility of the business model and Blockchain implementation's potential contributions.
Doctor of Philosophy (Ph.D.)
College of Engineering and Computer Science
Industrial Engineering and Management Systems
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Obeidat, Mohammad, "Assessing the Potential of Implementing Blockchain in Supply Chains using Agent-Based Simulation and Deep Learning" (2020). Electronic Theses and Dissertations, 2020-. 390.